Paper: Minimum-Risk Training of Approximate CRF-Based NLP Systems

ACL ID N12-1013
Title Minimum-Risk Training of Approximate CRF-Based NLP Systems
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2012

Conditional Random Fields (CRFs) are a pop- ular formalism for structured prediction in NLP. It is well known how to train CRFs with certain topologies that admit exact inference, such as linear-chain CRFs. Some NLP phe- nomena, however, suggest CRFs with more complex topologies. Should such models be used, considering that they make exact infer- ence intractable? Stoyanov et al. (2011) re- cently argued for training parameters to min- imize the task-specific loss of whatever ap- proximate inference and decoding methods will be used at test time. We apply their method to three NLP problems, showing that (i) using more complex CRFs leads to im- proved performance, and that (ii) minimum- risk training learns more accurate models.